1. Field of the Invention
Generally, the invention relates to automated decision-making and optimizing automated decision-making processes. More particularly, the invention relates to data processing systems and methods for simulating an analytic value chain.
2. Background Information
Businesses must make a multitude of decisions every day, both large and small. These decisions may involve determining what price to charge a particular customer, whether to grant a loan or an insurance policy, how to route air traffic or whether or not to issue a prescription to a particular patient. Particularly in financial services industries, entities have traditionally employed large numbers of low- and mid-level knowledge workers to make many of these decisions, a practice which often entailed high operation and opportunity costs to reach decisions. Additionally, traditional decision-making processes can be slow and cumbersome. For example, using traditional methods of mortgage underwriting, obtaining a loan approval often required several months. The human factor in decision-making can also result in imprecise, inconsistent decisions. Seeking to improve such factors in decision-making as cost, speed, consistency, precision and agility, businesses are turning more and more to automated decision-making technologies.
Using these technologies it becomes possible to build automated systems that sense data, apply codified knowledge or logic to the data, and make decisions with little or no human intervention. Additionally, the Internet has made automated decision-making more feasible. More and more individual financial data is obtainable over the Internet in real-time. For example, an individual's FICO (FAIR ISAAC CORPORATION, Minneapolis Minn.) score, which summarizes the consumer's credit relationships and payment history into one number, is available in a second or two. Consumers easily apply for loans online. Automated decision-making can help businesses generate decisions that are more consistent than those made by people and can help managers move quickly from insight to decision to action.
Since the early days of scoring and automated decision making, there has been a quest to improve data, models, and strategies, with the hope of improving decision yield, and thereby improving the profit picture and competitive capacity of a business operation. However, there are costs and risks associated with introducing changes such as analytic innovations to a current operation. Even limited field tests can be expensive to administer and businesses usually desire ROI (return on investment) estimates for proposed analytic innovations before proceeding to field testing.